https://github.com/fivetran/dbt_klaviyo_source
https://github.com/fivetran/dbt_klaviyo_source
dbt dbt-packages fivetran klaviyo
Last synced: 6 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/fivetran/dbt_klaviyo_source
- Owner: fivetran
- License: apache-2.0
- Created: 2021-03-26T00:21:48.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2025-01-22T20:58:01.000Z (9 months ago)
- Last Synced: 2025-03-29T05:34:01.225Z (6 months ago)
- Topics: dbt, dbt-packages, fivetran, klaviyo
- Language: Shell
- Homepage: https://fivetran.github.io/dbt_klaviyo_source/
- Size: 1.1 MB
- Stars: 2
- Watchers: 41
- Forks: 5
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
# Klaviyo Source dbt Package ([Docs](https://fivetran.github.io/dbt_klaviyo_source/))
## What does this dbt package do?
- Materializes [Klaviyo staging tables](https://fivetran.github.io/dbt_klaviyo_source/#!/overview/klaviyo_source/models/?g_v=1&g_e=seeds) which leverage data in the format described by [this ERD](https://fivetran.com/docs/applications/klaviyo#schemainformation). These staging tables clean, test, and prepare your Klaviyo data from [Fivetran's connector](https://fivetran.com/docs/applications/klaviyo) for analysis by doing the following:
- Names columns for consistency across all packages and for easier analysis
- Adds freshness tests to source data
- Adds column-level testing where applicable. For example, all primary keys are tested for uniqueness and non-null values.
- Generates a comprehensive data dictionary of your klaviyo data through the [dbt docs site](https://fivetran.github.io/dbt_klaviyo_source/).
- These tables are designed to work simultaneously with our [Klaviyo transformation package](https://github.com/fivetran/dbt_klaviyo).
## How do I use the dbt package?
### Step 1: Prerequisites
To use this dbt package, you must have the following:
- At least one Fivetran Klaviyo connection syncing data into your destination.
- A **BigQuery**, **Snowflake**, **Redshift**, **PostgreSQL**, or **Databricks** destination.
#### Databricks Dispatch Configuration
If you are using a Databricks destination with this package you will need to add the below (or a variation of the below) dispatch configuration within your `dbt_project.yml`. This is required in order for the package to accurately search for macros within the `dbt-labs/spark_utils` then the `dbt-labs/dbt_utils` packages respectively.
```yml
dispatch:
- macro_namespace: dbt_utils
search_order: ['spark_utils', 'dbt_utils']
```
### Step 2: Install the package
Include the following klaviyo_source package version in your `packages.yml` file.
> TIP: Check [dbt Hub](https://hub.getdbt.com/) for the latest installation instructions or [read the dbt docs](https://docs.getdbt.com/docs/package-management) for more information on installing packages.
```yaml
packages:
- package: fivetran/klaviyo_source
version: [">=0.7.0", "<0.8.0"]
```
### Step 3: Define database and schema variables
By default, this package runs using your destination and the `klaviyo` schema. If this is not where your Klaviyo data is (for example, if your Klaviyo schema is named `klaviyo_fivetran`), add the following configuration to your root `dbt_project.yml` file:
```yml
vars:
klaviyo_database: your_database_name
klaviyo_schema: your_schema_name
```
### (Optional) Step 4: Additional configurations
Expand for configurations
#### Unioning Multiple Klaviyo Connections
If you have multiple Klaviyo connections in Fivetran and would like to use this package on all of them simultaneously, we have provided functionality to do so. The package will union all of the data together and pass the unioned table into the transformations. You will be able to see which source it came from in the `source_relation` column of each model. To use this functionality, you will need to set either (**note that you cannot use both**) the `klaviyo_union_schemas` or `klaviyo_union_databases` variables:
```yml
# dbt_project.yml
...
config-version: 2
vars:
klaviyo_source:
klaviyo_union_schemas: ['klaviyo_usa','klaviyo_canada'] # use this if the data is in different schemas/datasets of the same database/project
klaviyo_union_databases: ['klaviyo_usa','klaviyo_canada'] # use this if the data is in different databases/projects but uses the same schema name
```
#### Passthrough Columns
Additionally, this package includes all source columns defined in the macros folder. We highly recommend including custom fields in this package as models now only bring in the standard fields for the `EVENT` and `PERSON` tables. You can add more columns using our passthrough column variables. These variables allow the passthrough fields to be aliased (`alias`) and casted (`transform_sql`) if desired, although it is not required. Datatype casting is configured via a SQL snippet within the `transform_sql` key. You may add the desired SQL snippet while omitting the `as field_name` part of the casting statement - this will be dealt with by the alias attribute - and your custom passthrough fields will be casted accordingly.
Use the following format for declaring the respective passthrough variables:
```yml
# dbt_project.yml
...
vars:
klaviyo__event_pass_through_columns:
- name: "property_field_id"
alias: "new_name_for_this_field_id"
transform_sql: "cast(new_name_for_this_field as int64)"
- name: "this_other_field"
transform_sql: "cast(this_other_field as string)"
klaviyo__person_pass_through_columns:
- name: "custom_crazy_field_name"
alias: "normal_field_name"
```
#### Changing the Build Schema
By default, this package will build the Klaviyo staging models within a schema titled (`` + `_stg_klaviyo`) in your target database. If this is not where you would like your Klaviyo staging data to be written to, add the following configuration to your `dbt_project.yml` file:
```yml
# dbt_project.yml
...
models:
klaviyo_source:
+schema: my_new_schema_name # leave blank for just the target_schema
```
> Note that if your profile does not have permissions to create schemas in your warehouse, you can set the `+schema` to blank. The package will then write all tables to your pre-existing target schema.
#### Change the source table references
If an individual source table has a different name than the package expects, add the table name as it appears in your destination to the respective variable:
> IMPORTANT: See this project's [`dbt_project.yml`](https://github.com/fivetran/dbt_klaviyo_source/blob/main/dbt_project.yml) variable declarations to see the expected names.
```yml
vars:
klaviyo__identifier: your_table_name
```
### (Optional) Step 5: Orchestrate your models with Fivetran Transformations for dbt Core™
Expand for more details
Fivetran offers the ability for you to orchestrate your dbt project through [Fivetran Transformations for dbt Core™](https://fivetran.com/docs/transformations/dbt). Learn how to set up your project for orchestration through Fivetran in our [Transformations for dbt Core™ setup guides](https://fivetran.com/docs/transformations/dbt#setupguide).
## Does this package have dependencies?
This dbt package is dependent on the following dbt packages. These dependencies are installed by default within this package. For more information on the following packages, refer to the [dbt hub](https://hub.getdbt.com/) site.
> IMPORTANT: If you have any of these dependent packages in your own `packages.yml` file, we highly recommend that you remove them from your root `packages.yml` to avoid package version conflicts.
```yml
packages:
- package: fivetran/fivetran_utils
version: [">=0.4.0", "<0.5.0"]
- package: dbt-labs/dbt_utils
version: [">=1.0.0", "<2.0.0"]
- package: dbt-labs/spark_utils
version: [">=0.3.0", "<0.4.0"]
```
## How is this package maintained and can I contribute?
### Package Maintenance
The Fivetran team maintaining this package _only_ maintains the latest version of the package. We highly recommend that you stay consistent with the [latest version](https://hub.getdbt.com/fivetran/klaviyo_source/latest/) of the package and refer to the [CHANGELOG](https://github.com/fivetran/dbt_klaviyo_source/blob/main/CHANGELOG.md) and release notes for more information on changes across versions.
### Contributions
A small team of analytics engineers at Fivetran develops these dbt packages. However, the packages are made better by community contributions.
We highly encourage and welcome contributions to this package. Check out [this dbt Discourse article](https://discourse.getdbt.com/t/contributing-to-a-dbt-package/657) to learn how to contribute to a dbt package.
## Are there any resources available?
- If you have questions or want to reach out for help, see the [GitHub Issue](https://github.com/fivetran/dbt_klaviyo_source/issues/new/choose) section to find the right avenue of support for you.
- If you would like to provide feedback to the dbt package team at Fivetran or would like to request a new dbt package, fill out our [Feedback Form](https://www.surveymonkey.com/r/DQ7K7WW).